At a high level, data quality means having the right data at the right time for a given task. High-quality data is accurate, complete, reliable, consistent, secure, and available in a timely manner.
Data quality plays a big role in getting software products to the market, and is vital to the development and training of artificial intelligence (AI) tools. For example, without a reliable source of quality data, it's difficult to thoroughly and efficiently test new features and bug fixes. And the longer it takes to test, the longer until the next release.
But getting that high-quality data into the hands of developers can be challenging. In particular, the treasure trove of data from customer interactions contains highly sensitive personal data that must be protected.
In this guide, we'll explain how data quality affects go-to-market speed, and what you can do to ensure that your developers have access to the secure, high-quality data that they need.
How data quality contributes to AI and software development
Let's take a look at some aspects of AI and software development that benefit from high-quality data.
Testing accuracy
When the development data closely mirrors actual data, developers can be more certain that their testing steps and scripts reflect the data that actual users would provide.
This helps them to more quickly and accurately identify and replicate bugs. They can be confident that an issue is an actual issue and not a side effect of poor data.
And with repeatable sets of data, developers can easily retest to verify that an issue has been fixed.
Testing velocity
Testing is an iterative process. Developers test, fix any issues, and then test again.
When developers can easily spin up a consistent set of secure, reliable data for each round of testing, they can complete their testing much more quickly.
Release velocity and product quality
New products and versions cannot be released until the testing is complete.
A faster testing process that is enabled by high-quality data translates directly to a faster release process.
And a more complete and accurate testing process means a higher quality product.
AI model training
Training an AI model requires large volumes of high-quality data, such as patient records or customer transcripts.
This ensures, for example, that a support chatbot points customers to the correct resources, or that a telehealth chat produces accurate healthcare recommendations.
In this case, the data quality is very closely tied to data security — ensuring that the data is scrubbed of all identifying information.
Data governance and compliance
All organizations are required to protect sensitive personal data. This is both an ethical and a legal responsibility.
High-quality data must always be secure.
Unblock product innovation with high-fidelity synthetic data that mirrors your data's context and relationships.
Overcoming barriers to high-quality data
So why can it be difficult to obtain high-quality data? And how can you use Tonic.ai's de-identification and temporary database products to overcome those barriers?
Data complexity
Databases can be highly complex, made up of multiple interconnected tables that contain tens of columns and millions of rows.
And data can come from a wide range of different sources — sales transactions, support calls, patient interactions, and so on.
How can developers get a reliable set of development data that replicates that complexity and variety, and that is a manageable size?
Tonic Structural's subsetting feature does just that. You specify the primary records that you want — such as five percent of the sales records or only the support calls from Ohio. Structural then uses that as the basis for a dataset that preserves all of the intricate relationships between the tables.
Data sensitivity
Another issue is data sensitivity. Organizations must closely protect sensitive personal information that is in their data, such as personally identifiable information (PII) and personal healthcare information (PHI).
So how can they provide developers with realistic data that does not contain any of those sensitive values?
Tonic Structural allows you to identify and then replace multiple types of sensitive values in databases and text-based files. Features such as Structural consistency and column linking also ensure that related values remain in sync.
Tonic Textual identifies and redacts values from a wide variety of file types, including PDFs and images. The redacted files can then be used as input for AI model training and development.
Data provisioning
Every new feature and every new round of testing requires a new set of data, ideally one that has the same structure and the same data as the one used for the previous round.
If a developer has to rely on a database administrator to provide their databases, it can cause a significant delay in development and testing.
Tonic Ephemeral allows you to quickly create and populate a new temporary database. You can even use de-identified Structural output as the basis for an Ephemeral snapshot, which can then be used to create a new, identical database to start each round of testing.
Data quality use cases for Tonic.ai
Here are some use cases that require high-quality data, and how they are supported by Tonic.ai.
Software development and testing
The development and testing process needs data that is as close as possible to production data. While it needs to be as realistic as possible, the data cannot contain sensitive personal data.
Testing is also an iterative process that requires multiple identical sets of data to start each round.
Tonic Structural allows you to generate realistic datasets in which all sensitive values are replaced. The generated data preserves the data relationships. You can also generate smaller or larger subsets of data for different uses.
Tonic Ephemeral allows you to quickly create a new, temporary database. One source for Ephemeral data is Structural output. Once the output is generated, you can use it at the start of each test to spin up the exact same database. You can also set up an expiration timer in Ephemeral to automatically spin down a database once it is no longer needed.
AI model training
To properly train an AI model requires a large volume of realistic data. But that data must be secure — to prevent data leakage and ensure regulatory compliance, you cannot use patient notes and support transcripts that reveal names, identifiers, and other sensitive information.
Tonic Structural can identify and replace sensitive values in databases and text-based files.
Tonic Textual can do the same thing for unstructured, free-text data, including other file types such as PDFs and images.
RAG system development
Retrieval augmented generation (RAG) allows you to augment a large language model (LLM) with additional data. The additional data usually takes the form of text from documents. However, as with the previous use cases, the data must be secure — it cannot contain sensitive information.
Tonic Textual can identify and replace sensitive values in a variety of free-text file types. It can also provide a streamlined output format that is easy to use in your RAG development.
Conclusion
Getting a well-tested and reliable product to market quickly, or training a new AI model, requires easy and reliable access to high-quality data. High-quality data is accurate, consistent, and, most importantly, secure.
Tonic Structural, Textual, and Ephemeral allow you to quickly generate (and re-generate) realistic datasets that replace sensitive values while optimizing for data utility.
To learn more about Structural de-identification, Textual file redaction, and Ephemeral temporary databases, connect with our team today.
Janice Manwiller is the Principal Technical Writer at Tonic.ai. She currently maintains the end-user documentation for all of the Tonic.ai products and has scripted and produced several Tonic video tutorials. She's spent most of her technical communication career designing and developing information for security-related products.
